Literature DB >> 25398608

SNV-PPILP: refined SNV calling for tumor data using perfect phylogenies and ILP.

Karen E van Rens1, Veli Mäkinen2, Alexandru I Tomescu2.   

Abstract

MOTIVATION: Recent studies sequenced tumor samples from the same progenitor at different development stages and showed that by taking into account the phylogeny of this development, single-nucleotide variant (SNV) calling can be improved. Accurate SNV calls can better reveal early-stage tumors, identify mechanisms of cancer progression or help in drug targeting.
RESULTS: We present SNV-PPILP, a fast and easy to use tool for refining GATK's Unified Genotyper SNV calls, for multiple samples assumed to form a phylogeny. We tested SNV-PPILP on simulated data, with a varying number of samples, SNVs, read coverage and violations of the perfect phylogeny assumption. We always match or improve the accuracy of GATK, with a significant improvement on low read coverage.
AVAILABILITY AND IMPLEMENTATION: SNV-PPILP, available at cs.helsinki.fi/gsa/snv-ppilp/, is written in Python and requires the free ILP solver lp_solve. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
© The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

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Mesh:

Year:  2014        PMID: 25398608     DOI: 10.1093/bioinformatics/btu755

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  2 in total

1.  Somatic mutation detection and classification through probabilistic integration of clonal population information.

Authors:  Fatemeh Dorri; Sean Jewell; Alexandre Bouchard-Côté; Sohrab P Shah
Journal:  Commun Biol       Date:  2019-01-31

2.  MIPUP: minimum perfect unmixed phylogenies for multi-sampled tumors via branchings and ILP.

Authors:  Edin Husić; Xinyue Li; Ademir Hujdurović; Miika Mehine; Romeo Rizzi; Veli Mäkinen; Martin Milanič; Alexandru I Tomescu
Journal:  Bioinformatics       Date:  2019-03-01       Impact factor: 6.937

  2 in total

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